Region mask.

As introduced before, conventional cycle training suffered from the ill-posed penalty.
As the top-n prediction results of classification and regression branch are likely to be inaccurate in initial training stage. Generated new templates feature based on these top-n boxes may do not contain any target object feature. Forcing such template to track back to the initial target position leads to the ill-posed penalty.
And the feature selection operation like RoI-Align is not differentiable on boxes coordinates, causing intermediate frames tracking error cannot be penalized.
Although Precise RoI-Pooling [1]} can be used to make the coordinate differentiable, it cannot pass the gradient to all the candidate boxes.
To address these issues, it is necessary to introduce a module to that can select target features from last search region involving all estimated boxes and be differentiable on the coordinates.
